Semantic-Segmentation Inference for Computer Agriculture Canopy Nitrogen Estimation Under Spectral-Drift Confounding: An Artificial Intelligence Domain-Adaptation Error-Bound Evaluation

Spectral drift in canopy imaging shifts the relationship between reflectance and nitrogen content, so a semantic-segmentation model trained on one acquisition regime can return biased nitrogen estimates when deployed under another. The framework decomposes the target-domain risk of a segmentation-based nitrogen estimator into three terms: a source-risk term, an integral probability metric measuring spectral-domain discrepancy, and a label-noise term induced by drift. A before-after-control-impact design is used to characterise the power of the bound-based evaluation across cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points, with 2000 replications per design cell and an intraclass correlation of 0.64. The analysis is a design and power evaluation, not an observational study: no field data are used or claimed. Under the declared variance structure (between-cluster SD = 6.0, residual SD = 4.5), 54 clusters are required to detect a 5 percentage-point effect at 80% power. The contribution is a bound that makes spectral-drift confounding explicit in the evaluation of segmentation-based nitrogen inference, together with a design analysis that tells practitioners how many clusters such an evaluation needs before its conclusions are stable.